@drahmadbazzi Great work. Please, how can I code an optimization problem that aims to maximize the sum rate while optimizing the beamforming and position of antennas? It is a non-convex problem. Also, how can I draw system architecture like the one you posted?
NIGERIA UPDATE: Our party, the SDP has issued a statement with regards the oppressive conduct of the Kaduna State government and misuse of federal security agencies in the persecution of opposition leaders.
We must all stand up to oppression and abuse of power to safeguard our democracy and sovereignty of our country. - @elrufai
How do LLMs actually work?
LLMs power AI applications like ChatGPT, Grok, and Claude to generate human-like text and assist with complex tasks.
Here’s a simple to understand breakdown of how they work:
Step 1) Learning from massive text data
LLMs train on huge datasets (books, websites, and code) to recognize patterns and relationships between words. This text is cleaned and broken into tokens—small pieces that a machine can process.
Step 2) Training the model
Using transformers (a deep learning technique), LLMs analyze contextual relationships between words. They improve over time by adjusting their internal settings (weights) through gradient descent—a trial-and-error process that minimizes mistakes.
Step 3) Fine-tuning for special tasks
After training, LLMs are fine-tuned for specific applications like coding, or customer support. This is done using supervised learning, Reinforcement Learning from Human Feedback (RLHF), or Low-Rank Adaptation (LoRA) to improve accuracy.
Step 4) Generating responses
When you enter a prompt, the LLM processes your input, predicts the most likely next tokens, and generates a response. To improve accuracy and relevance, some models use Retrieval-Augmented Generation (RAG)—which searches external knowledge sources (like databases or documents) before generating a response to provide more factual answers. The LLM then applies decoding strategies like beam search and nucleus sampling to refine the final output.
Step 5) Filtering & optimization
Before deployment, LLMs go through safety filters to remove bias and harmful content. They are also optimized using techniques like quantization and pruning, making them efficient for cloud-based and on-device AI.
What are the challenges?
LLMs face issues like hallucinations (false outputs), bias, and high computational costs. Engineers optimize them using RAG, speculative decoding, hybrid cloud-edge deployment and other solutions.
LLMs aren’t magic—they’re pattern-recognition machines built with techniques that are continually evolving.
💭 What is your favorite LLM powered application? 💬
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PBAT ordered the payment of 4 of the 8 months' withheld salaries & the 25% & 35% salary increments. So, who's disobeying the presidential order to pay lecturers?
Dear Finance Minister & the AGF, who is sitting on lecturers' salaries?
#LetTheLecturersBreathe#LecturersLifeMatter
@officialABAT@AjuriNgelale@channelstv@ARISEtv @NTANewNow
Mr president, your passion for education can't be completed without proper care for the lecturers. Why not addressing the issues that led to ASUU's strike since? Address it with speed it deserves NOW.
@officialABAT Happy New Year our Father and the Commander in Chief.
Mr. President lecturers are suffering, dying, and moving abroad:
1. No payment of withheld salaries.
2. No payment of 35% and arrears.
3. No payment of 40% peculiar allowance.
4. Even two months palliative.